class_docstr: Tests the correctness of the Y = g(X) = Log[1 + exp(X)] transformation.
code: def testVariableHingeSoftness(self):
      hinge_softness = tf.Variable(1.0)
      b = softplus.Softplus(
          hinge_softness=hinge_softness, validate_args=True
      )
      self.evaluate(hinge_softness.initializer)
      self.assertIs(hinge_softness, b.hinge_softness)
      self.assertEqual((), self.evaluate(b.forward(0.5)).shape)
      with self.assertRaisesOpError(
          "Argument `hinge_softness` must be non-zero."
      ):
          with tf.control_dependencies([hinge_softness.assign(0.0)]):
              self.evaluate(b.forward(0.5))
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 18 for calling the SoftplusBijectorTest obj's underlying member method code with example usage: obj.testVariableHingeSoftness() without return types